Microaneurysms (MAs) detection is a critical step in diabetic retinopathy screening, since MAs are the earliest
visible warning of potential future problems. A variety of algorithms have been proposed for MAs detection
in mass screening. Different methods have been proposed for MAs detection. The core technology for most of
existing methods is based on a directional mathematical morphological operation called "Top-Hat" filter that
requires multiple filtering operations at each pixel. Background structure, uneven illumination and noise often
cause confusion between MAs and some non-MA structures and limits the applicability of the filter. In this paper,
a novel detection framework based on edge directed inference is proposed for MAs detection. The candidate MA
regions are first delineated from the edge map of a fundus image. Features measuring shape, brightness and
contrast are extracted for each candidate MA region to better exclude false detection from true MAs. Algorithmic
analysis and empirical evaluation reveal that the proposed edge directed inference outperforms the "Top-Hat"
based algorithm in both detection accuracy and computational speed.
A robust and computationally efficient algorithm is proposed for
optic disk detection in retinal fundus images. The algorithm
includes two steps: optic disk localization and boundary detection.
In the localization step, vessels are modeled as a tree structure
and the root of the vessel tree is detected automatically and served
as the location of an optic disk. The implementation is based on an
efficient multi-level binarization and A* search algorithm. In the
boundary detection step, a circle is used to model the shape of an
optic disk, and Radon transform is applied to estimate the center
and radius of the circle. Experimental results of 48 retinal
images with varying image qualities show 100% accuracy in
localization and an accuracy of 92.36% in boundary detection. The
success of the proposed algorithm is attributed to the robust
features extracted from retinal images.
Fingerprint verification has been deployed in a variety of
security applications. Traditional minutiae detection based
verification algorithms do not utilize the rich discriminatory
texture structure of fingerprint images. Furthermore, minutiae
detection requires substantial improvement of image quality and is
thus error-prone. In this paper, we propose an algorithm for
fingerprint verification using the statistics of subbands from
wavelet analysis. One important feature for each frequency subband
is the distribution of the wavelet coefficients, which can be
modeled with a Generalized Gaussian Density (GGD) function. A
fingerprint verification algorithm that combines the GGD
parameters from different subbands is proposed to match two
fingerprints. The verification algorithm in this paper is tested
on a set of 1,200 fingerprint images. Experimental results
indicate that wavelet analysis provides useful features for the
task of fingerprint verification.